The invention relates in general to signal data processing and in particular to processing a sequence of input data for a audio system.
In the field of transmission of signals for a digital television system, data from different data sequences are assigned to individual channels. Each channel is assigned a transmission frequency or frequency band. Due to relatively scarce frequency resources, the individual channels are situated close to each other, which leads to superpositioning of signals or signal components, especially with respect to the sequences of input data for a receiver. Problems arise in particular with regard to an efficient use of a linear-phase adaptive filter or a filtering method for static data with white noise superimposed on it. Known filters and filtering methods for processing data or signals with multiple sinusoidal signal components of static or slowly changing frequency in an environment with white noise do not offer satisfactory results. This holds especially for the use of an adaptive compensation filter, known as an adaptive line enhancer (“ALE”), for the channel evaluation of pilot-based orthogonal frequency division multiplex (“OFDM”) systems.
The problem of suppression of noise on sinusoidal measurement data without knowledge of its content or frequency distribution is generally handled by a form of prediction. Usually an adaptive linear prediction is carried out, making use of an ALE. Most such adaptive filters are used to correct a single narrow-band signal in broad-band noise. However, adaptive filters are also known for correction of multiple repeatedly superimposed narrow-band signals.
To eliminate the noise, these adaptive filters use pure forward-looking prediction devices or prediction filters, for example a forward linear predictor (“FLP”). The taps or parameters of all these devices are individually adjusted and optimized. Both these forward-looking prediction filters and also backward-looking prediction filters, for example a backward linear predictor (“BLP”), have the drawback that they cannot guarantee any linearity of the phase. In general, they develop minimum-phase or maximum-phase tap set solutions, which minimizes their prediction error. One thus achieves the criterion of the minimal “Wiener Hopf” error, which means that important parts of the developed response or the corrected spectral lines have a linear in-phase behavior when compared with the original signal, i.e., the input signal.
The principles are described in Simon Haykin, Adaptive Filter Theory, 4th ed., 2002, Prentice Hall, Chapter 3 (Linear Prediction), Section 5.3 (ALEs), and in Interference Suppression In Spread Spectrum Code Division Multiple Access Communications, (Rusch, L. A.), 1994, Department of Electrical of Engineering, Princeton University. In the latter the system output values are not a prediction in itself, but rather the error of a prediction, to detect an interference but not to detect a signal.
Such adaptive filters are generally known from European patent application EP 1334554 and corresponding U.S. published patent application US 2002191688, which relate to a lattice predictive filter implemented by a noise subtraction using a notch filter.
There have also been a number of technical articles on this subject. For example, Adaptive Enhancement of Multiple Sinusoids in Uncorrelated Noise, Zeidler J. R., et al., 1978, IEEE Trans. Acous. Speech Signal Process, Vol. ASSP-26, pp. 240-254, deals with forward-looking adaptive predictive filters in a general survey article. Bi-directional Wiener filters are described in Performance Analysis of LMS Adaptive Prediction Filters, Zeidler, J. R., 1990, Proc. IEEE, Vol. 78, pp. 1781-1806.
It is known to use two coupled predictive devices in a double forward cascade layout, where the dependent device, or slave prefilter, ultimately converges to an IIR solution. This is inconvenient for finite data sets, but effective for individual continuous sinusoidal frequency adjustments that change slowly in terms of frequency, from Adaptive Line Enhancer with Self Tuning Prefilter, Coi, Y. S., Shin, H. C., Song, W. J., 2003, Communication & Signal Processing Lab., Division of Electronic and Computer Engineering, Pohang University, Korea.
An Efficient Implementation of Forward-Backward Least-Mean-Square Adaptive Line Enhancers, Yeh, H. G., Nguyen, T. M., 1995, TDA Progress Report 42-121 merely describes a forward-looking and backward-looking determination of a predictive error to improve the convergence behavior in conjunction with individual predictive filtering. The quality benefit of the tap adjustment, however, may impair the capability of a single predictor when the tap update rate is much less than the tap/error accumulation rate.
Block Implementation of Forward Backward Line Enhancer, Farhang-Boroujeny, B. Lim, Y. C., 1991, describes the use of forward-looking and backward-looking error determinations to improve the convergence.
Forward-Backward LMS Adaptive Line Enhancer, Lim, Y. C. Ko, C. C., IEEE Trans, 1990, CAS-37, pp. 936-940 describes an adaptive prediction algorithm. This uses forward-looking and backward-looking prediction errors to update the coefficient values. For a given feedback factor, the algorithm converges on the optimal Wiener solution with the same speed as that of the LMS algorithm, but it requires twice the number of multiplications and additions. However, if the order of magnitude of the prediction filter is at least slightly larger than the number of sinusoids being corrected, or if the frequencies of the sinusoidal signal components being corrected do not lie close to 0 or 0.5, this algorithm leads to only half the size of the adjustment error as the LMS algorithm. Thus, it is evidently an individual prediction filter that uses forward-looking and backward-looking errors to improve the convergence behavior. A bi-directional Wiener filter for suppression is generally known from Gupta, A. K., On Suppression of Sinusoidal in Broadband Noise, IEEE Trans. Acoustics, Speech and Signal Processing, Vol. ASSP-33, pp. 1024-1026, August 1985.
The drawback to the known solutions is that the linear prediction filters all use individually adaptable taps, i.e., not dependent taps, which are all coordinated with minimum-phase or maximum-phase systems. All single predictors have asymmetrical behavior at the two ends of input data sequences with a limited length. The fact that multiple sinusoidal signal components can be predicted backward and forward is merely used to improve the convergence behavior and to correct a tap misadjustment in the converged state. But the use of forward-looking and backward-looking error components for the tap misadjustment does not offer much improvement to the convergence and the misadjustment when the tap update rate is substantially less than the tap error accumulation rate divided by the order of magnitude or length of the prediction filter. In this case, the advantage in tap adjustment quality from using both prediction errors is sacrificed and results in deterioration of the measure of an adaptive single predictive filter.
What is needed is an improved method and adaptive filter for processing a sequence of input data for a radio system.